from sklearn_benchmarks.report import Reporting, ReportingHpo
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = Reporting(config_file_path="config.yml")
reporting.run()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 39.0 | 54.384087 |
| daal4py_KNeighborsClassifier | 0.0 | 2.0 | 30.456924 |
| KNeighborsClassifier_kd_tree | 0.0 | 2.0 | 35.909501 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 0.0 | 27.515845 |
| KMeans_tall | 0.0 | 0.0 | 21.163132 |
| daal4py_KMeans_tall | 0.0 | 0.0 | 8.017377 |
| KMeans_short | 0.0 | 0.0 | 2.684944 |
| daal4py_KMeans_short | 0.0 | 0.0 | 1.336918 |
| LogisticRegression | 0.0 | 0.0 | 18.183558 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 3.792476 |
| Ridge | 0.0 | 0.0 | 10.171015 |
| daal4py_Ridge | 0.0 | 0.0 | 1.843034 |
| HistGradientBoostingClassifier | 0.0 | 5.0 | 8.638117 |
| lightgbm | 0.0 | 5.0 | 3.034249 |
| xgboost | 0.0 | 5.0 | 28.505828 |
| catboost | 0.0 | 5.0 | 0.341623 |
| total | 1.0 | 7.0 | 16.059444 |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.165 | 0.000 | 4.835 | 0.000 | 1 | 5 | NaN | NaN | 0.468 | 0.000 | 0.354 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 21.851 | 0.147 | 0.000 | 0.022 | 1 | 5 | 0.825 | 0.938 | 1.822 | 0.021 | 11.990 | 0.159 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.176 | 0.000 | 0.000 | 0.176 | 1 | 5 | 1.000 | 1.000 | 0.080 | 0.001 | 2.205 | 0.015 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.120 | 0.000 | 6.659 | 0.000 | -1 | 100 | NaN | NaN | 0.451 | 0.000 | 0.266 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 32.625 | 0.000 | 0.000 | 0.033 | -1 | 100 | 0.930 | 0.827 | 1.775 | 0.018 | 18.377 | 0.188 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.156 | 0.014 | 0.000 | 0.156 | -1 | 100 | 1.000 | 1.000 | 0.079 | 0.001 | 1.970 | 0.176 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.121 | 0.000 | 6.634 | 0.000 | -1 | 1 | NaN | NaN | 0.451 | 0.000 | 0.267 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 22.924 | 0.110 | 0.000 | 0.023 | -1 | 1 | 0.715 | 0.717 | 1.803 | 0.027 | 12.712 | 0.202 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.154 | 0.016 | 0.000 | 0.154 | -1 | 1 | 1.000 | 1.000 | 0.081 | 0.001 | 1.896 | 0.199 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.121 | 0.000 | 6.595 | 0.000 | 1 | 100 | NaN | NaN | 0.453 | 0.000 | 0.268 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 22.191 | 0.063 | 0.000 | 0.022 | 1 | 100 | 0.930 | 0.827 | 1.784 | 0.015 | 12.438 | 0.111 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.177 | 0.000 | 0.000 | 0.177 | 1 | 100 | 1.000 | 1.000 | 0.080 | 0.000 | 2.214 | 0.012 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.118 | 0.000 | 6.762 | 0.000 | -1 | 5 | NaN | NaN | 0.450 | 0.000 | 0.263 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 32.879 | 0.000 | 0.000 | 0.033 | -1 | 5 | 0.825 | 0.717 | 1.815 | 0.024 | 18.112 | 0.239 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.160 | 0.014 | 0.000 | 0.160 | -1 | 5 | 1.000 | 1.000 | 0.080 | 0.001 | 1.995 | 0.180 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.117 | 0.000 | 6.820 | 0.000 | 1 | 1 | NaN | NaN | 0.449 | 0.000 | 0.261 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 12.271 | 0.087 | 0.000 | 0.012 | 1 | 1 | 0.715 | 0.938 | 1.874 | 0.042 | 6.549 | 0.154 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.172 | 0.001 | 0.000 | 0.172 | 1 | 1 | 1.000 | 1.000 | 0.080 | 0.001 | 2.146 | 0.042 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.053 | 0.000 | 0.303 | 0.000 | 1 | 5 | NaN | NaN | 0.097 | 0.000 | 0.546 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 17.844 | 0.023 | 0.000 | 0.018 | 1 | 5 | 0.977 | 0.986 | 0.323 | 0.005 | 55.331 | 0.831 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.024 | 0.000 | 0.000 | 0.024 | 1 | 5 | 1.000 | 1.000 | 0.006 | 0.000 | 4.340 | 0.147 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.053 | 0.000 | 0.304 | 0.000 | -1 | 100 | NaN | NaN | 0.097 | 0.000 | 0.541 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 28.692 | 0.138 | 0.000 | 0.029 | -1 | 100 | 0.981 | 0.984 | 0.269 | 0.003 | 106.785 | 1.275 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.030 | 0.002 | 0.000 | 0.030 | -1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 5.830 | 0.396 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.054 | 0.000 | 0.294 | 0.000 | -1 | 1 | NaN | NaN | 0.097 | 0.000 | 0.564 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 20.473 | 0.075 | 0.000 | 0.020 | -1 | 1 | 0.973 | 0.971 | 0.266 | 0.001 | 77.009 | 0.515 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.018 | 0.002 | 0.000 | 0.018 | -1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 3.621 | 0.452 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.061 | 0.000 | 0.263 | 0.000 | 1 | 100 | NaN | NaN | 0.096 | 0.000 | 0.635 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 17.812 | 0.024 | 0.000 | 0.018 | 1 | 100 | 0.981 | 0.984 | 0.269 | 0.003 | 66.188 | 0.640 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.024 | 0.000 | 0.000 | 0.024 | 1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 4.513 | 0.370 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.054 | 0.000 | 0.298 | 0.000 | -1 | 5 | NaN | NaN | 0.097 | 0.000 | 0.552 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 28.812 | 0.122 | 0.000 | 0.029 | -1 | 5 | 0.977 | 0.971 | 0.264 | 0.002 | 109.002 | 1.087 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.029 | 0.001 | 0.000 | 0.029 | -1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 5.379 | 0.208 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.058 | 0.000 | 0.275 | 0.000 | 1 | 1 | NaN | NaN | 0.098 | 0.000 | 0.596 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 9.584 | 0.027 | 0.000 | 0.010 | 1 | 1 | 0.973 | 0.986 | 0.316 | 0.003 | 30.323 | 0.257 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.015 | 0.000 | 0.000 | 0.015 | 1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 2.766 | 0.113 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.955 | 0.000 | 0.027 | 0.000 | 1 | 100 | NaN | NaN | 0.712 | 0.000 | 4.152 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.694 | 0.046 | 0.000 | 0.005 | 1 | 100 | 0.978 | 0.956 | 0.098 | 0.001 | 48.071 | 0.828 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.001 | 0.000 | 0.002 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 9.533 | 4.780 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.901 | 0.000 | 0.028 | 0.000 | -1 | 100 | NaN | NaN | 0.749 | 0.000 | 3.872 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.737 | 0.021 | 0.000 | 0.003 | -1 | 100 | 0.978 | 0.973 | 0.550 | 0.008 | 4.972 | 0.085 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 100 | 1.000 | 1.000 | 0.001 | 0.000 | 5.153 | 1.995 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.906 | 0.000 | 0.028 | 0.000 | -1 | 1 | NaN | NaN | 0.716 | 0.000 | 4.056 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.438 | 0.003 | 0.000 | 0.000 | -1 | 1 | 0.959 | 0.975 | 0.182 | 0.002 | 2.402 | 0.035 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.627 | 2.382 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.943 | 0.000 | 0.027 | 0.000 | 1 | 5 | NaN | NaN | 0.711 | 0.000 | 4.140 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.402 | 0.010 | 0.000 | 0.001 | 1 | 5 | 0.980 | 0.973 | 0.546 | 0.006 | 2.566 | 0.034 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 1.262 | 0.581 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.023 | 0.000 | 0.026 | 0.000 | 1 | 1 | NaN | NaN | 0.692 | 0.000 | 4.370 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.741 | 0.005 | 0.000 | 0.001 | 1 | 1 | 0.959 | 0.975 | 0.180 | 0.002 | 4.108 | 0.048 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 2.175 | 1.192 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.109 | 0.000 | 0.026 | 0.000 | -1 | 5 | NaN | NaN | 0.655 | 0.000 | 4.749 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.837 | 0.007 | 0.000 | 0.001 | -1 | 5 | 0.980 | 0.956 | 0.098 | 0.001 | 8.571 | 0.150 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 10.150 | 4.234 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.792 | 0.000 | 0.020 | 0.000 | 1 | 100 | NaN | NaN | 0.460 | 0.000 | 1.722 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.053 | 0.000 | 0.000 | 0.000 | 1 | 100 | 0.985 | 0.977 | 0.001 | 0.000 | 70.901 | 22.598 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 5.923 | 4.719 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.776 | 0.000 | 0.021 | 0.000 | -1 | 100 | NaN | NaN | 0.480 | 0.000 | 1.615 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.044 | 0.001 | 0.000 | 0.000 | -1 | 100 | 0.985 | 0.990 | 0.007 | 0.000 | 6.449 | 0.499 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 18.825 | 14.080 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.700 | 0.000 | 0.023 | 0.000 | -1 | 1 | NaN | NaN | 0.484 | 0.000 | 1.447 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.025 | 0.001 | 0.001 | 0.000 | -1 | 1 | 0.972 | 0.990 | 0.001 | 0.000 | 21.860 | 5.912 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 19.111 | 14.763 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.701 | 0.000 | 0.023 | 0.000 | 1 | 5 | NaN | NaN | 0.441 | 0.000 | 1.588 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.023 | 0.000 | 0.001 | 0.000 | 1 | 5 | 0.985 | 0.990 | 0.006 | 0.000 | 3.633 | 0.254 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 4.744 | 3.779 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.711 | 0.000 | 0.023 | 0.000 | 1 | 1 | NaN | NaN | 0.451 | 0.000 | 1.575 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.021 | 0.001 | 0.001 | 0.000 | 1 | 1 | 0.972 | 0.990 | 0.001 | 0.000 | 19.182 | 4.891 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 4.776 | 3.688 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.726 | 0.000 | 0.022 | 0.000 | -1 | 5 | NaN | NaN | 0.484 | 0.000 | 1.500 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.027 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.985 | 0.977 | 0.001 | 0.000 | 36.529 | 11.912 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 19.421 | 15.326 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.564 | 0.0 | 0.851 | 0.000 | random | NaN | 30 | NaN | 0.398 | 0.0 | 1.418 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.408 | 0.000 | random | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 7.846 | 4.634 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.712 | 7.018 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.530 | 0.0 | 0.906 | 0.000 | k-means++ | NaN | 30 | NaN | 0.409 | 0.0 | 1.294 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.413 | 0.000 | k-means++ | 0.001 | 30 | 0.000 | 0.000 | 0.0 | 7.060 | 3.757 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.995 | 7.136 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.010 | 0.0 | 3.993 | 0.000 | random | NaN | 30 | NaN | 2.717 | 0.0 | 2.212 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 15.197 | 0.000 | random | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.683 | 2.235 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.019 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.215 | 5.548 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.024 | 0.0 | 3.984 | 0.000 | k-means++ | NaN | 30 | NaN | 2.841 | 0.0 | 2.120 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 15.000 | 0.000 | k-means++ | 0.001 | 30 | 0.002 | 0.000 | 0.0 | 5.744 | 2.455 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.020 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.541 | 6.171 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 20 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.230 | 0.000 | 0.014 | 0.000 | k-means++ | NaN | 20 | NaN | 0.086 | 0.0 | 2.674 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.000 | 0.190 | 0.000 | k-means++ | 0.002 | 20 | -0.000 | 0.001 | 0.0 | 2.622 | 0.493 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.000 | 0.000 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 10.158 | 7.072 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.077 | 0.000 | 0.041 | 0.000 | random | NaN | 20 | NaN | 0.031 | 0.0 | 2.469 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.000 | 0.179 | 0.000 | random | 0.001 | 20 | 0.001 | 0.001 | 0.0 | 2.721 | 0.617 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.000 | 0.000 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.831 | 6.855 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.588 | 0.000 | 0.272 | 0.000 | k-means++ | NaN | 20 | NaN | 0.359 | 0.0 | 1.636 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.001 | 4.847 | 0.000 | k-means++ | 0.321 | 20 | 0.301 | 0.001 | 0.0 | 2.985 | 0.846 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.000 | 0.013 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 6.524 | 3.541 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.204 | 0.000 | 0.783 | 0.000 | random | NaN | 20 | NaN | 0.135 | 0.0 | 1.516 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.000 | 7.046 | 0.000 | random | 0.290 | 20 | 0.346 | 0.001 | 0.0 | 1.856 | 0.312 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.000 | 0.013 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 6.842 | 3.639 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 10.158 | 0.0 | [-0.11614659] | 0.000 | NaN | NaN | NaN | NaN | NaN | 1.793 | 0.0 | 5.666 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [52.59448598] | 0.000 | NaN | NaN | NaN | NaN | 0.543 | 0.000 | 0.0 | 0.889 | 0.482 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.22393031] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.000 | 0.0 | 0.436 | 0.368 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [26] | 0.746 | 0.0 | [2.78722331] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.680 | 0.0 | 1.097 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [26] | 0.002 | 0.0 | [129.27821548] | 0.000 | NaN | NaN | NaN | NaN | 0.330 | 0.003 | 0.0 | 0.571 | 0.114 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [26] | 0.000 | 0.0 | [20.78100614] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.001 | 0.0 | 0.153 | 0.104 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | deprecated |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.177 | 0.000 | 0.451 | 0.0 | NaN | NaN | NaN | 0.181 | 0.0 | 0.980 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.010 | 0.000 | 8.055 | 0.0 | NaN | NaN | 0.098 | 0.017 | 0.0 | 0.593 | 0.013 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.000 | 1.191 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.652 | 0.614 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.365 | 0.000 | 0.586 | 0.0 | NaN | NaN | NaN | 0.231 | 0.0 | 5.908 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.001 | 1.921 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.0 | 1.695 | 3.610 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.000 | 0.013 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.721 | 0.707 | See | See |
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}
reporting_hpo = ReportingHpo(files=[
"results/benchmarking/sklearn_HistGradientBoostingClassifier.csv",
"results/benchmarking/xgboost_XGBClassifier.csv",
"results/benchmarking/lightgbm_LGBMClassifier.csv",
"results/benchmarking/catboost_CatBoostClassifier.csv"
])
reporting_hpo.run()